{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "505b334b",
   "metadata": {},
   "source": [
    "#### 1.4.1 直方图与核密度估计\n",
    "\n",
    "- 直方图：\n",
    "```\n",
    "对于数据分布，常用直方图（histogram）进行描述。将数据取值的范围分成若干区间（一般是等间隔的），在等间隔的情况下，每个区间长度称为组距，考察数据落入每一区间的频数与频率，在每个区间上画一个矩形，它的宽度是组距，他的高度可以是频数、频率或频率/组距，在高度是频率/组距的情况下，每一矩形的面积恰是数据落入区间的频率，这种直方图可以估计总体的概率密度。组距对直方图的形态有很大的影响，组距太小，每组的频数较少，由于随机性的影响，邻近区间上的频数可能很大；组距太大，直方图所反映的形态就不灵敏。\n",
    "```\n",
    "\n",
    "- 核密度估计：\n",
    "```\n",
    "核密度估计（kernel density estimation）使用已知样本估计总体的密度函数，在概率论中用来估计未知的密度函数，属于非参数检验方法之一。\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "db2163c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# interp1d  一维插值\n",
    "from scipy.interpolate import interp1d\n",
    "import matplotlib.mlab as mlab\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import scipy.stats as st\n",
    "\n",
    "# 设置字体为楷体\n",
    "plt.rcParams['font.sans-serif'] = ['KaiTi']\n",
    "# 用来正常显示负号\n",
    "plt.rcParams['axes.unicode_minus'] = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6ef8c60d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 数据的直方图、核密度估计曲线\n",
    "\n",
    "\n",
    "# 体重数据\n",
    "weights = np.array([75.0, 64.0, 47.4, 66.9, 62.2, 62.2, 58.7,\n",
    "                   63.5, 66.6, 64.0, 57.0, 69.0, 56.9, 50.0, 72.0])\n",
    "\n",
    "# 数据去重\n",
    "weight_set = np.array(list(set(weights)))\n",
    "\n",
    "# 生成高斯核密度估计（要求数据没有重复值）\n",
    "kd = st.gaussian_kde(weight_set)\n",
    "# dens = kd.evaluate(weight_set)\n",
    "\n",
    "# 通过高斯核密度估计计算概率密度\n",
    "dens = kd.pdf(weight_set)\n",
    "# 插值法，50表示插值个数，个数 >= 实际数据个数，一般来说插值个数越多，曲线越平滑\n",
    "x_new = np.linspace(min(weight_set), max(weight_set), 50)\n",
    "# 生成插值：'cubic'表示三次样条插值（Cubic Spline Interpolation）\n",
    "f = interp1d(weight_set, dens, kind='cubic')\n",
    "y_new = f(x_new)\n",
    "\n",
    "# 画出密度估计曲线\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.plot(x_new, y_new, color='r', label='密度估计曲线')\n",
    "\n",
    "# 绘制直方图\n",
    "# weights 样本数据\n",
    "# color   方块的颜色\n",
    "# density True显示概率密度，False显示频数\n",
    "plt.hist(weights, color='steelblue', density=True, label='直方图', edgecolor='k')\n",
    "\n",
    "# 根据体重数据的最大、最小值生成该范围内的正态分布密度函数值\n",
    "x_norm = np.arange(np.min(weight_set), np.max(weight_set), 1)\n",
    "# x_norm 样本数据，np.mean(x_norm) 均值，st.tstd(x_norm)) 无偏的标准差\n",
    "plt.plot(x_norm, st.norm.pdf(x_norm, np.mean(x_norm), st.tstd(x_norm)),\n",
    "        color='g', label='正态分布曲线')\n",
    "\n",
    "plt.legend(fontsize=12)\n",
    "plt.show()\n",
    "\n",
    "# Pandas\n",
    "# 使用pandas计算密度估计曲线，效果也很好\n",
    "dx = pd.Series(weight_set)\n",
    "plt.figure(figsize=(8, 6))\n",
    "# kind='kde' 指定核密度估计(kernel density estimation)\n",
    "dx.plot(kind='kde', label='Pandas密度曲线估计')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "# 可以看到，通过pandas拟合的核密度估计和scipy的曲线一致，都是高斯核密度估计"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85d861ff",
   "metadata": {},
   "source": [
    "#### 1.4.2 经验分布函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "077a9977",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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69GH37t2XtY+JiaFXr15MmTLF8R0mInJBvpluMj8bNmwY/v7+lC1blsjISEaPHs3p06epUaMGPXr0ICEhgcqVK1OlSpX0XmDjxo0BmDZtGgEBAXz22Wf4+fnh5eVF0aJFSUxM5OWXXyYpKYlhw4ZRv3799O1t27aNSZMmMXv2bBITE5k4cSIhISF0797d4Zrr1asHpAV0mTJlcHFxuWwbjrr77rsZOHAgd911F0uXLr3sgrRDhw4xbNgwVqxYwdy5c3n55ZeBtMFNZsyYkeFwnyEhIenfUXt7e7N8+XJGjBiBv78/GzZsAKBfv36cOHGCMWPGsGDBAiDtD5ADBw4AcObMGRYvXpx+b3JMTEyGZyKaNGnC+++/n/61A6QN3vLPf/6TWrVqAVC0aFF++uknRo8ezTvvvMPgwYNp164dYWFhdO3alV69etG1a9cs7zcREay1eeanZcuWNjO7du3K9LWCLCYmxq5du9bh5ePi4uzSpUttampq+nNDhw61bdq0sVFRUVne/rx58+yOHTuy3O5KCxYssNHR0Zc9l5SUlP7/c+bMuey1OXPm2Pvuu++q9Xz//fd25syZ6Y8TEhIy3WZoaKhDtSUnJ9vhw4c7tKyjYmNj7fz587NlXYX12JfCrX37tJ984SaLBTbaDDLRpL2WN7Rq1cpu3Lgxw9d2795NgwYNcrmigiEpKema31Vfy9GjR6lUqdINt78ZCQkJGd7PXNjo2JfCqEOHtP/mj4FKOqT99waLNcZsstZedaGOTnEXAjcTro4O6ZkTFM4iUpgpoEVERG5AXGIymw5Hsa7aHbhYy/BsXr8CWkRExAFJKalsOXKGNSHh/BESztajZ0hOtbhWbstdZ7I6p9T1KaBFREQyYK0l5NQ5Vu8LZ01IOOsPRBCbmIKLgSZVyzLwrpr8o+YttBzsR6nUJOCFbN2+AlpEROSC2PPJrN4Xzm+7w/jfvtOERZ8HwPuWEjzcogp31PbktpqelClxybU9qUk5UosCWkRECrWjkXGs+OsUv/11inX7I0hMScWjWBHuquPFHXU8uaO2J9XKl8j1uhTQkqlTp07h6emZ5dHDMnIzt3rlpJSUlEzH7r5ZefU9ixR21lp2HIvm5x0n+G33KfaExQBQ07MkfW+rQacGFWnlXQ431xwdbPO6nLv1Qmbv3r3Zsp7XX3+d8PBwh5YNCwvj+eefZ/v27VnezqRJk3jjjTey3O5KUVFR3HXXXWzevNmh5ZcuXcqOHTscXv/gwYP54YcfHFo2MjKSgIAAYmLSfiGHDh3K1KlTHd7WleLj44mIuHok2507d3L77bdn+JqI5D5rLVuPnuHdn3Zz18SVPDBtDZ/97wDlSroxqnsDVgxvz4qXOzDq/obcVusWp4czKKBz1eDBg7l0IJawsDDmzp3Lgw8+yKhRo65aft68eUyaNInHH388fRallJQUZs2ahSMDzISEhFCuXDkmT57M1KlTMx3bOjMXh7UMDw9n8+bNzJs3jy+++CJLcxsnJSXRvXt3nnnmGVq0aHHNZePj43nnnXd4+umnmTFjhsPbKFWqVPo42BERERn+8bJ582b8/f355JNPeOeddzh37hyQ9kdT3bp1HdpOVFQUixcvvmymr1GjRtGuXTuio6MvW7ZRo0a89NJLdOvWjfPnzzv8XkQk+1hr2XIkinE/7uKO8SvpMf0PZqw5SE3PUox/pAkbX+/Mt4NuY8CdNanpVer6K8xlOsWdS2JjY7n11lupUKECH3/8MW3btmX58uW0a9eOgwcP8vzzz1/VpnLlytxzzz3UrFmTI0eOADB37lzeeOMNJk6cyNtvv53pNImQ1jusUaMGn376KWPGjOGpp55izpw5V815DGkH8rvvvktYWBiRkZHEx8ezf/9+Jk+eTKVKlShfvjylS5fG1dWVPXv2ODyAyZtvvknHjh3x9/fP8PXNmzcza9YsqlSpQmJiIrfffjsjR47M0mlnLy8vNmzYgJ+fH7GxsTz++OP07t37smWaNm2a/sdC5cqVufXWWzl+/DjlypVj3bp1uLm50alTpwzXP2rUKEqXLk25cuX4v//7PyZPnsx9993HpEmTaNiwIe+9916Gp7L9/PwICQnh9ddfTx/3W8SZAgIgMNDZVTim2YYA+rkH3tBExYeKlmWBV0MWejbkcLFyuKWmcMfZQwyL3MM9kfsp+2fC9VeSFcHB4OubvetEAZ1rPvvsM0aNGsW8efNISEigZcuWtGzZkokTJ+Lv70+XLl2uarN27Vo8PDw4fvw4LVq0ICYmhu+//565c+eyfft2Hn30Ub7++ms8PDyuahsaGkpISAhfffUVkBb2gwYNolOnTixbtix9FqeLjDEMHDiQiIgIatWqxZAhQ5gxYwbLli2jWrVqDB06NMvvOSYmhrlz57Jr165Mlylfvjy//fYbO3bsyHTKx+txc3OjT58+PPjgg5kuU6RIETw8PLDWps+h/e677zJixAhatWpF3759mT9/PuPGjaNcuXKXta1WrRrVq1enW7duBAYG0rNnT7Zv386iRYvYtGnTNWt77bXXaNCgAa+//vpV6xXJbYGBOZYl2a6feyANEoMBX4eWjypSjB9uqc/3no3Y4lEZYy23Rx/h+WNr6RIZQpmUHDyT5esLF2bTy075O6CHDUs72nKTry9MnpylJqtWrUqf8/jgwYMEBAQAsGzZMrZs2ULVqlV57bXXePrpp6ldu3Z6u2LFiuHt7c1nn33GY489xrRp0/jwww+Jj4/HWkv79u1p2bIl06ZNuyzgU1NTGTJkCDNnzsTLyyv9+QceeIAlS5Zw2223MWfOHNq0aXNZnV5eXnh5ebF69WpSUlLo1KkT+/fvp0iRIsTFxXHy5Elq1qzp8Pv+/fff6dy58zV7+ampqVSsWJH4+HhCQ0PZu3cvu3fvZvPmzaxfv5677rqLWbNmXXM7Li4unDhxgv/+97/ExsbSs2fPDIcJtdayZcsWli9fjo+PDykpKen7YNasWfTu3RsfHx+eeeYZhg4dStWqVYG0GblGjx7NH3/8QefOnYG0ma4qVqzI3r17+fPPP4mPj2fgwIEUKXL5r1SRIkW47777WLFiBY888ojD+04kp/j65pfxrQF8r1lsckoqK/ec5ruNR1m15xRJKZZ6FT14tUUVevhW5tYyxTNtmx/k74DOJ9q0aUNwcDAvv/wyH374IQDfffcdO3bsYM6cORhjmD59Om3atGHJkiW0a9cOSOuBli5dGg8PD86dO0ffvn355ZdfCA4Opk2bNvzrX/+iQoUKrFmzBm9vb+rWrYu1lhEjRvDss89y5513AmmTTqxZs4agoCCGDx/OkSNH6NKlC126dGHy5MmXTQEZERHB2LFjSUlJYfz48QQFBeHm5kZ4eDiffvop48aNo1evXg6979DQUGrUqHHNZQ4dOsTZs2f55z//Sbly5WjUqBFNmjThoYceombNmpme6k5ISKBNmzb06dOHEiVKUKlSJcqWLctjjz3Gjh07mDhx4lVtXFxc6NixI1u2bGH69Ok89NBDLF68mFKlSjF//nzmzp3L2bNncXV1Te9lA9SpU4f+/fvz7bffMmXKFMaOHUtYWBj79u1j0aJFNGvWjAYNGlwVzhfVqFEjS9/bi8i1hUbF8V3QUeZuPEpY9Hm8PIrif7s3DzevSoNbPW74bFxek78DOos9WWcpUaIEo0ePZuvWrcyZM4fOnTvTokULHn/8cfbs2cPq1atJTk5m/fr1hIWFAWk9y6VLl1K9enUqVqxI165d+euvv/D392ffvn3UrVuXPn36MHXq1PRTp6mpqQQEBNC/f38WLlzIokWLCAoKwt/fnwYNGrB06VJWrFjBr7/+mmGdcXFxDBgwgGnTpjF27FhGjhzJrFmzKF68OD179mTbtm1Ur17d4fddunRpDh8+fM1l9u7dy8CBAxk8ePBlz2/evJmff/6Z+++/P8N2xYoVw8fHh0cffRQfHx8Ajh07RpMmTXjvvfcybJOQkICPjw8PPvgg7du356effiIkJIR+/foxfvx4gKu+n1+5ciWRkZF069aNt99+m3feeYdXXnmF0qVL07lzZ0aMGHHd/RAdHX3dP1RE5NqSUlL5bXcY32w4yv/2nQagQ10v3upRnbvrV6BIHrjqOrvl74DOR1xcXJgxY0Z6mOzfv59XXnmFr7/+mnfeeYcnn3wST09P6tSpk77877//TmxsLHFxcaxZs4YlS5ZQs2ZNhg8fzsKFC9m+fftlPT0XF5f0oGvYsCGnT5+mcePGPP3005QsWZLZs2fTp0+fDOsLCwtjwoQJTJo0CW9v78uuVE5OTubMmTOcPHmSKlWqOPyeGzZsyOeff37NZX7++Wfq1avHwIEDcXNzIzU1lejoaE6ePMnKlSt58803GT16dIZtO3TowDfffIO7uztnz57l1KlTdO3aNdNed2pqKgD33HMPkDaVZv369SlXrlymp+GbNGnCCy+8wEMPPUTLli3Tr6YPCgrCw8OD6dOn89xzz13zPa5du5Zu3bpdcxkRyVhYdAJfrztM4IajhJ87z61livHC3XV4vHU1qpTN36ewr0cBnUtGjhzJsmXLWLhwIf369aNWrVpMmDCB3bt30717dzw9PdOXjYmJYcyYMVhrufvuu6lSpQrNmjWjRo0afPHFF7z11lusWLGCf//739ccCOOjjz5izJgxlCxZEoAjR47QtGnTDJc9fvw4H3zwAQAnT55Mv4jMGIObmxvjxo1j7969WepB+/r6EhERwcaNG2nV6qqpTtmzZw9Fixbl0Ucf5csvv+Tjjz9Of23Hjh3ce++9vPjii1e1i4uLo0SJEvTt25fjx49Tp04dUlNTKVEibaSfH3/8kU6dOl32PXRERMRVwf3bb7/h5+eHMSbTU2Kenp6UL1+eY8eOsWPHDlauXEmHDh2YMmUKn3zyCVOnTuWLL75gwIABGbbfvn07hw4dom3bttffYSKSbkupW5lVqQU/vreCFGu5u14Fev+jOu3rVsDVpWCcwr4ua22e+WnZsqXNzK5duzJ9La+bNGmSveWWW+zUqVNtSkpK+vPR0dG2W7dudunSpXbWrFl23Lhx9o8//rDWWrt9+/b05aZOnWqXLl1qrbU2KirKWmvt5MmTbUJCgo2IiMhwm1u2bLE9evSwqamp1lprz507Z6tXr+5wzX/99Ze11tqvvvrKfv/99zYlJcXu3r3b8Td9wW+//Wbr1auXXvdFKSkp9oknnrBHjx6158+ft4888kj6a+fPn7dffvml/eCDDzJc5wsvvGCttfbgwYN29OjR1lprv/jiC9urVy9rrbUzZ8601apVs1u2bElv88svv9hnn302/fH27dvtwIED0x937tz5qu1s27bNPvjgg7ZNmzb2/ffft6GhodZaaydMmGB//PFHa621CQkJtlGjRvbJJ5+86hiNjo62jRo1sj///PO1dtF15edjX/KW9u3TfvKq80kpduGWUPvgtDW2xsgfbOPh8+2YxTvtofBzzi4tRwEbbQaZqB50Lhg2bBj+/v6ULVuWyMhIRo8ezenTp6lRowY9evQgISGBypUrU6VKlfReYOPGjQGYNm0aAQEBfPbZZ/j5+eHl5UXRokVJTEzk5ZdfJikpiWHDhlG/fv307W3bto1JkyYxe/ZsEhMTmThxIiEhIXTv3t3hmuvVqweknTYvU6YMLi4ul23DUXfffTcDBw7krrvuYunSpekXpF287ezildJnz57l3nvvTR8es0KFCvTo0eOq9YWEhLBy5UoAvL29Wb58OSNGjMDf358NGzYA0K9fP06cOMGYMWNYsGABAM2bN+fAgQMAnDlzhsWLF6ffmxwTE5PhmYgmTZrw/vvvp3/tAH8P3lKrVi0AihYtyk8//cTo0aN55513GDx4MO3atSMsLIyuXbvSq1cvunbtmuX9JlKYRCckMWfdYWb9cYhTMeep6VmSMQeX88jpHZR6P+NrZgqFjFLbWT8FtQd9M2JiYuzatWsdXj4uLs4uXbo0vedsrbVDhw61bdq0uaoX64h58+bZHTt2ZLndlRYsWGCjo6OttdYmJyfbc+cu/4v4Yu/0er7//ns7c+bM9McJCQmZLuvoOpOTk+3w4cMdWtZRsbGxdv78+dmyrsJ67Ev2y2s96LCz8fadn3bZxv+31NYY+YN98ot1dsVfYTYlJTXvFZuDyKQHbawDQ0bmllatWtlLh8K81O7du2nQoEEuV1Qw3MykDUePHqVSpUqa9MGJdOxLdunQIe2/zr4P+mB4LAH/O8D8TaEkp6ZyX5NbGdy+Fo2rXHIXRV4pNhcYYzZZa6+6UEenuAuBmwlXR4f0FBG5nr9ORvPRbyH8tOMEbq4uPNaqKoPuqkmNW0o6u7Q8SQEtIiI5as/JGKb8tpeftp/Eo2gRhrSvhX87byp4XD3in/xNAS0iIjlib1gMU37bx0/bT1DSvQhD765N/zt8KFsi8+F/5W8KaBERyVYhp2KYvHwfP24/QQk3V57rUJsBdyqYs0oBLSIi2eLE2Xgm/bqX/24KpbibK0Pa12LgnTUpV1LBfCMU0CIiclPOxiXxye/7+fKPg1gL/dr58FzH2pRXMN8UBXQ+kZCQwMGDB3P1dptTp07h6emJi0vBG4ReRG5eQlIKX609xPSV+4lOSOJh3yq8dE9dqpUv4ezSCgQFdD7h6upKs2bNiIqKSh9b2xFhYWG89dZbPPPMMzRp0iRL25w0aRIuLi6MGzcuq+WKSAGWmmpZtPUYE5fu4fjZBNrX9WJk1/o0rFza2aUVKApoJ4qIiGDdunVs2rSJRx55hEaNGmW6bGxsLK1ataJkyZKkpqZy7tw5Spe+9i9DSEgI1atXZ/LkyQwZMoTx48dTvnx5h+vbsGEDEyZMIDw8nCNHjrB///70ITl1f7RI4RR89Axjluxky5EzNK1ahvcfa8bttT2v31CyTAGdyzZv3kxgYCBbtmwhMjKSe+65h3/84x94e3tnuHxKSgrHjh1j6dKlnDt3jieffJKTJ09SoUIFJkyYkD6WdUaGDh1KjRo1+PTTTxkzZgxPPfUUc+bMuWrOY0gb8vXdd98lLCyMyMhI4uPj2b9/P5MnT6ZSpUqUL1+e0qVL4+rqyp49exTQIoVMWHQC45f+xfebj+HlUZQPHmvGw82r4FJYZpZyAgV0LmvRogUtWrQgKiqKpk2bMmHChEyXDQ0NZcqUKdxyyy0sWrSIsWPHsmfPHubMmXPd7YSGhhISEsJXX30FQOXKlRk0aBCdOnVi2bJl6dNJXmSMYeDAgURERFCrVi2GDBnCjBkzWLZsGdWqVWPo0KE398ZFJF1AAAQG5v52m20IoJ97IHRwvE2CcWXGra2YXuUfJBsXhpzYyHPH1lHq16QcqxOA4GDw9c3ZbeRx+TqgxyzZya7j0bm6zYaVSzP6gcxPRTuqXLly1z1FXbVqVSZOnMju3bvZsmULDz30EH/88Qd9+/Zl+vTpeHh4ZNguNTWVIUOGMHPmTLy8vNKff+CBB1iyZAm33XYbc+bMoU2bNpe18/LywsvLi9WrV5OSkkKnTp3Yv38/RYoUIS4ujpMnT1KzZs2bfu8ihV1goHPyp597IA0SgwHHNvxb2Zq86d2Jo8XK0iVyH68fXkWN82dysMJL+PqCn1/ubCuPytcBnd+sXLmSL7/8Mr1Xm5yczKhRo/jtt9/o378/AwYMuKrN8ePHefXVVxkxYgT9+vXj008/JTQ0lEaNGvHiiy/Su3dvKlWqlL68tZYRI0bw7LPPcueddwJpV4CvWbOGoKAghg8fzpEjR+jSpQtdunRh8uTJ6VNAQtr34mPHjiUlJYXx48cTFBSEm5sb4eHhfPrpp4wbN45evXrl8J4SKfh8fZ0wD0QHgOtv+NiZeMYs3skvu8KoU6EUcx5oxB11ugPDcrpCuUS+Dujs6MnmptatWzN8+PD0xx4eHgwaNIixY8dedSuTtZZffvmFbdu28dVXX1GmTBmOHTtGVFQUPXv2pEKFCvz000/p80TXqVOH1NRUAgIC6N+/PwsXLmTRokUEBQXh7+9PgwYNWLp0KStWrODXXzOeXzUuLo4BAwYwbdo0xo4dy8iRI5k1axbFixenZ8+ebNu2jerVq+foPhIR50lKSWXWH4eYtHwvqdYysmt9+t/hg3sR3WrpDPk6oPObUqVKXXZRV6VKlTINPGstd999N/fee2/6c5s2baJkyZLcf//9VKxYkaioKB5//HHq1KkDgIuLC4MHDwagYcOGnD59msaNG/P0009TsmRJZs+eTZ8+fTLcXlhYGBMmTGDSpEl4e3uTkJCQ/lpycjJnzpzh5MmTVKlS5ab3g4jkPZsOR/L6gh38dTKGTvUr8OaDjXQ/s5Ppz6JcdultTsWLF890ORcXF958800AVq9eDcDw4cPZsGEDkBbAjz/+OL179yY5OTnDdXz00UeMGTMm/b7pI0eO0LRp0wyXPX78OB988AHe3t6cPHky/SIyYwxubm6MGzeOvXv3qgctUsCcjU/i399v45FP1nI2PonP+rTki6daKZzzAPWgc0lCQgJHjx7lxIkTfPzxx5w4cYK1a9dy3333ce7cOU6ePElUVBStW7fmxx9/JCoqip9++olx48axatUqSpUqRfPmzRk7diwA3333Hffffz9//fVXhiN9BQcHs23bNsaMGQOk3Ud94MABKlSokGF9zZs3T///SpUqpZ+Kd3Fxwc3NjfHjx9O/f3+NKiZSgPy6K4zXF2wnIjaRgXf6MKxzXUoWVSzkFfqXyGGpqamMHDmS/fv307BhQ/z8/Khduzbdu3fn//7v/3Bzc8uw3cGDB3niiScA+Ne//kX37t1p27Yt7dq1w8vLi02bNjFu3DgWLlyIj4/PZW23bdvGpEmTmD17NomJiUycOJGQkBC6d+/ucN316tUD0gK6TJkyuLi4UL9+/RvcCyKSl0TGJvLm4p0s3nqc+pU8mOnfmsZVrh4fQZxLAZ3DXFxcmDhxYpbbtWzZkpYtWwJQsmRJfvnlF2bPns0PP/xATEwMZcqUYeTIkVedco6Pj+fEiRPMmjULY9IGEDh16hS7d+9m2bJlWa6jaNGiVKxYMcvtRCTvscCP5esx+sPfiU5I4l/31GVw+1q6CCyPUkDnE+7u7vTv35/+/ftfc7nixYtfdmEZwAcffJBpT/162rZte9ltXCKSP52KTuCNuj1YVr4uzcoVZ8Kj/6BepYzHUpC8QQFdCNxoOAMa0lOkAFiy9TijFu4goawP/z68iv7jxlPEVb3mvE4BLSJSQJ2NS+KNRTtYvPU4zauX5f21n1ArIQoUzvlCvgpoa23696oihYG11tklSD61Zl84L8/bSvi58wy/py5DOtSiyHdRzi5LsiDfBLSbmxvx8fGUKKF786TwiI+Pv6mvKKTwSUhKYfzSv/jyj0PU8irJ533b0aSqrtDOj/JNQFeoUIFjx45RpUoVihcvrp60FGjWWuLj4zl27JiuoheH7Th2lmFzgwk5dQ7/2715tVt9irm5OrssuUH5JqAvzvx0/PhxkpJyeJozkTzAzc2NihUrXnfWM5HUVMvMPw4yfulflC/pzuz+bbizjtf1G0qelm8CGtJCWh9WIiJ/izh3nuHztrJqz2nubVSR8Y80pWwJd2eXJdnAoUv5jDGNjTFBxpgoY8xE48D5ZWNMP2PMEWNMnDFmrTEmf009JSKSx/0ZEk63Kav5c38Eb/VoxKdPtlQ4FyDXDWhjTFFgCbAOaAU0Afyv06YWMAZ4GKgLbAS+uclaRUQESE5J5YNf9tB7xnpKFSvCwmfb0ec2b12bU8A4coq7G1ASeNlae94Y8yowDfjyGm2aA+ustZsAjDGfAdceAktERK7r2Jl4XvxmCxsPR/FYy6qM6dGIEu756ttKcZAj/6rNgDXW2vMXHm8FGl6nzS6gozHGF9gPvAj8eqNFiojkVwEBEBh4+XPNNgTQzz0QOmRtXSvL+jCsdneSjSuTD/zCQ+t2w/QsrCA4GHx9s7ZRcRpHAro0cPDiA2utNcakGGPKWWszvOvdWrvLGDMf2HLhqZNAm4yWNcYMAgYBmmtYRAqcwMCrc7GfeyANEoMB3wzbXCkFw5SqtzO16u00iD3Fx/sW4ZNwJuvF+PqCn1/W24lTOBLQyUDKFc8lACWADAPaGNMGeBC4HdgJvAQsM8b4WmsTL13WWhsABAC0atVKwyaJSIHj6wurVl3yRAeAK5/MWGRsIi9+u4XV+8J5tGVV3n6oK8Xc+uVAlZLXOBLQkUDtK57zABIzWPaiXsA31tq1Fx6PMcYMJi2wV2W1SBGRwij46BmenbOJ8NhE3vtnE3q2rqYLwQoRRwI6COhz8YExxgcoSlpwZ8YF8LykTQnSQl1D2oiIXIe1ljnrjzB2yU4qli7G/MG3a7jOQsiRgP4fUMEY42etDQReA5Zba1OMMa7W2itPfwOsBv5jjNlC2vfP/kAssD6b6hYRKZDiE1N4bcF2Fmw5Rsd6Xkzq6at7mwup6wa0tTbZGDMA+MYYMwWw/H3t4QZjzHvW2nlXNJsP1AOGApWBvcDD1tpz2Va5iEgBc+xMPIO+2siuE9H86566PN+xNi4uOqVdWDl085y1dvGFwUdaknZ/c8SF51tmsrwFxl34ERGR69hwMJIhczaRmJzKzKda07F+BWeXJE7m8N3t1tqTwI85WIuISKH09frDjF60k+rlSxDQtxW1K5RydkmSB2j4GRERJ0lMTmXMkp18vf4IHep5MeWJ5pQprvm/JY0CWkTECcKLlODZL9az4VAkz7SvySv31sdV3zfLJRTQIiK5bGeJCgyq9xDhoWeY8oQvPXyrOLskyYMU0CIiuejXXWG80KgXZZLPM2/wbTStWtbZJUke5dB80CIicnOstXyx+gCDZm+kdnwki3fMVjjLNSmgRURymCWVNxbt4O0fd9OlYUXm7vqWCkmxzi5L8jgFtIhIDkp1TSKs/kbmrDvCM+1r8knvlpRITXJ2WZIP6DtoEZEcEhoVx4lGG0kqdo73/tmEJ9poSl1xnAJaRCQHbDkSxcCvNpHsnkLFv9rwRBvP6zcSuYQCWkQkmy3beZIXvtlCxdLFcN/RFvcED2eXJPmQvoMWEclGs9ceYsicTTS4tTQLnr1d4Sw3TD1oEZFsYK1l4rI9fLxqP50bVOCjXi0o7u7q7LIkH1NAi0iBERAAgYG5v11rUgmvuY1Yr2OUCqvGvnWN6fZl2gnKZhsC6Oce+PckvQDBweDrm/uFSr6iU9wiUmAEBqZlX25KdUkmrN5GYr2OUfZoXW452ARzyUdrP/dAGiReUZSvL/j55Wqdkv+oBy0iBYqvL6xalTvbOhWTwNOzgjh2IoYJDzfl8dbVrl6oA0AuFiUFhgJaROQGHDh9jqe+3EB4TCJf9G1Fx/oVnF2SFDAKaBGRLNp69Az+X27AGMM3g/6Bb7Wyzi5JCiAFtIhIFvwZEs7ArzZSrqQ7s/u3xcezpLNLkgJKAS0i4qBlO08yNHAL3p4lmN2/LRVLF3N2SVKAKaBFRBwwb+NRRs7fRtOqZZnVrzVlS7g7uyQp4BTQIiLXMWPNQd76YRd31Pbksz4tKVlUH52S83SUiYhkwlrLh7/u5aMVIXRrXInJT/hStIhGB5PcoYAWEclAaqrlzSU7+WrtYXq2qsa4hxtTxFVjO0nuUUCLiFwhKSWVl+dtZVHwcZ65qyavdquPMcbZZUkho4AWEblEQlIKzwduZvnuU7zStR7Pdqjt7JKkkFJAi4hcEJ+YwqDZG1m9L5y3ejSiz23ezi5JCjEFtIgIcO58MgP+E8T6g5FMeCSTcbVFcpECWkQKveiEJPxnbmBr6Fkm9/Slh28VZ5ckooAWkcItKjaRvjM38NfJaKb7Nadr41udXZIIoIAWkUIs/Nx5nvxiPQfCY/msT0vurl/R2SWJpFNAi0ihFBadgN/n6zh2Jp6ZT7Xmjjqezi5J5DIKaBEpdEKj4uj9xXrCY87z1dNtaeNT3tkliVxFAS0ihcqRiDh6fb6O6IQkZg9oS4vq5ZxdkkiGFNAikq8EBEBgYMavNdsQQD/3QOiQ8euHi5alV8OexLm48c3u72i86t0cqzNdcDD4+ub8dqTA0cCyIpKvBAamZV5G+rkH0iAx4xcvDefA3XNpHHcqx2q8jK8v+PnlzrakQFEPWkTyHV9fWLUqgxc6AFz94uGIWHoFrCMuKYXAAf+gYeVHc7hCkZunHrSIFGhXh3NpZ5ck4hAFtIgUWApnyc8U0CJSICmcJb9TQItIgaNwloJAAS0iBcrhomUVzlIgKKBFpMBIv5VK4SwFgAJaRAqEIxFxf9/nrHCWAkABLSL53rEz8fT6fB2xrm58vfs7hbMUCBqoRETytYuzUkXHJxG4ex6NcmuEMJEcph60iORbp2PO4/f5OsJjzjPr6TY0iQ1zdkki2UY9aBHJlyJjE3nyi/UcOxPPf/q1oWUNzUolBYsCWkTynRTXJPrMWM/BiFhmPtWatjVvcXZJItlOAS0i+UqqaxJh9TdwMiyGgL6tuKOOp7NLEskRCmgRyTfiEpMJqxdEYsmzBPi1oGO9Cs4uSSTH6CIxEckXEpJSGPCfjZz3iMIrxJcujSo5uySRHOVQQBtjGhtjgowxUcaYicYY4+gGjDHjjTFLbrxEESnszien8MzsTaw9EIHn/maUjKzs7JJEctx1A9oYUxRYAqwDWgFNAH9HVm6MaQo8Cwy74QpFpFBLSknlua+38Pve07z7cBNKhVd1dkkiucKRHnQ3oCTwsrV2P/Aq0P96jYwxLkAAMOlCOxGRLElOSWXYt8Es3x3G2B6NeKJNdWeXJJJrHAnoZsAaa+35C4+3Ag0daDeYtN72IWPMgxd64lcxxgwyxmw0xmw8ffq0Q0WLSMGXmmoZOX87P24/wajuDeh7m7ezSxLJVY4EdGng4MUH1loLpBhjMh0VwBhTChgDHABqAC8BfxpjSl65rLU2wFrbylrbysvLK6v1i0gBZK3lrR93MX9zKMM612HAnTWdXZJIrnPkNqtkIOWK5xKAEkBUJm3+Sdpp8Y7W2nBjTBFgO/AU8PEN1ioihcSU3/bx5R+H6NfOmxc71XF2OSJO4UgPOhK4smvrASReo01VIMhaGw5grU0GtgE+N1KkiBQeM9ccZPLyfTzasipvdG9IFm4aESlQHAnoIOC2iw+MMT5AUdKCOzOhpPWwL1WDS06Vi4hc6b+bQhn7wy7ubVSR9/7ZBBcXhbMUXo4E9P+ACsYYvwuPXwOWW2tTjDGumbT5EahrjHnWGFPVGPMC4Av8dNMVi0iBtGznSUbO30a72rcw5YnmFHHVOEpSuF33N+DC6ekBwOfGmNNAD2DkhZc3GGMey6BNBHAf0AfYS9p90E9Yaw9lT9kiUpD8ERLO0MAtNKlShoA+rSjmltnf/iKFh0NjcVtrFxtjagEtgXUXAhhrbctrtPmDS06Ni4hkJPjoGQZ+tREfz5LM6teakkU1RYAIZGGyDGvtSdJOXYuIZIs9J2Pw/3IDnqWKMrt/G8qWcHd2SSJ5hr7kERGnOBIRR58Z63F3deHrAW2pULqYs0sSyVN0LklEct2p6ASenLGexJRUvnvmNqqVv/KmDxFRD1pEctWZuET6zNhAxLnzzOrXhroVPZxdkkiepB60iOSa2PPJ+H8ZxMGIWGb5t8a3WllnlySSZ6kHLSK5IiEphUGzN7L92Fmm9WrO7bU9nV2SSJ6mgBaRHJecksoL32zhj5AIJjzSlC6NKjm7JJE8TwEtIjnq4rSRv+wK480HGvJIy6rOLkkkX1BAi0iOuXTayJc618W/nebLEXGUAlpEcszFaSOfbufDC51qO7sckXxFAS0iOeLSaSNHdW+gaSNFskgBLSLZTtNGitw83QctItkqo2kjAwIgMDB71t9sQwD93AOhQwYvBgeDr2/2bEjEydSDFpFsk9m0kYGBadmZHfq5B9IgMZOV+fqCn1/Gr4nkM+pBi0i22HIk6prTRvr6wqpV2bChDgDZtTKRvEs9aBG5aWnTRgZp2kiRbKSAFpGbcnHayKJFNG2kSHbSKW4RuWFh0Qn0nrFO00aK5AD1oEXkhqRNG7meyHOJmjZSJAeoBy0iWXZx2shDEXGaNlIkh6gHLSJZomkjRXKHAlpEHKZpI0VyjwJaRByiaSNFcpcCWkSuS9NGiuQ+BbSIXJemjRTJfQpoEbkmTRsp4hwKaBHJ1MVpI7s2qqRpI0VymQJaRDJ0cdrIO2p7MqWXL0Vc9XEhkpv0GyciV7k4bWTTqmX4rE9LihZxdXZJIoWOAlpELnPptJFf+l89baSI5A4FtIik07SRInmHAlpEAE0bKZLX6NyViGjaSJE8SAEtkkcFBEBgYM5vJ6VIIicbrie5aCKVdv2DQSuyf9rIZhsC6OceCB2yYWXBweDrmw0rEsnbdIpbJI8KDEzLopyU6pJMWP0gkorFUWFPK4rGls2R7fRzD6RBYnD2rMzXF/z8smddInmYetAieZivL6xalTPrTkhK4elZGzl28CwBvVvQpVEOThvZAcA3596MSAGkHrRIIZSUksrQb7bw5/4I3n9M00aK5EUKaJFCJjXV8sp/t/HrrjDG9mjEw801baRIXqSAFilErLW8uWQnC7YcY8S99eh7m7ezSxKRTCigRQqRD37Zy1drD/PMXTV5tkMtZ5cjIteggBYpJD77fT/TVobQq001Xu1WX9NGiuRxCmiRQiBw/RHe/fkv7m96K28/1EThLJIPKKBFCrjFW4/z+sLtdKznxYeP++KqOZ1F8gUFtEgBtuKvMP41N5jW3uX5uHdL3IvoV14kv9Bvq0gBte5ABEPmbKbBraWZ8VQrirtrTmeR/EQBLVIAbQs9w4D/bKRa+RL85+k2eBRzc3ZJIpJFCmiRAmZfWAxPzdxA2RJuzOnflvIlNaezSH6kgBYpQI5GxvHkjPUUcXVhTv+2VCqjOZ1F8isFtEgBcSo6gd5frCchKZXZ/dvg7VnS2SWJyE1QQIsUAFGxifSZsYHwc+eZ1a819SuVdnZJInKTNN2kSD4XnZBE35kbOBgRy5f+rWlevZyzSxKRbOBQD9oY09gYE2SMiTLGTDRZHIbIGLPUGON/QxWKSKZizyfT78sgdp+I5tMnW9Cudg7O6Swiueq6AW2MKQosAdYBrYAmgL+jGzDG9AbuvcH6RCQTCUkpDPxqI1uORDG1V3Purl/R2SWJSDZypAfdDSgJvGyt3Q+8CvR3ZOXGmPLAB8CeG65QRK5yPjmFwXM2sfZABO8/1oz7mtzq7JJEJJs58h10M2CNtfb8hcdbgYYOrv8DYAFQ/AZqE5EMJKek8uI3wazac5p3Hm7CP1tUdXZJIpIDHOlBlwYOXnxgrbVAijHmmleiGGM6Ap2AkddZbpAxZqMxZuPp06cdKEek8EpJtQyft5WlO0/yxv0N8Wtb3dkliUgOcSSgk4HzVzyXAJTIrIExphjwGTDEWht9rZVbawOsta2sta28vLwcKEekcLLW8vqC7SwKPs6Ie+vR/w4fZ5ckIjnIkYCOBK5MTg8g8Rpt3gCCrLU/3mhhIvI3ay1jluzi26CjPN+xNs91rO3skkQkhznyHXQQ0OfiA2OMD1CUtODOjB/gZYw5c+FxCeBxY8yj1tr7b7BWkULJWsuEZXuY9echnm7nw/AudZ1dkojkAkcC+n9ABWOMn7U2EHgNWG6tTTHGuFprUzJoc+cV636ftNu05tx0xSKFzLQVIXyyaj9+bavzxv0NyOIwBCKST103oK21ycaYAcA3xpgpgAU6XHh5gzHmPWvtvCvahF762BhzDgi31p7MnrJFCoeztx7gg1/38s/mVXi7R2OFs0gh4tBQn9baxcaYWkBLYJ21NuLC8y0dbO9/wxWKFFLRFQ8RVWM39zWpxIRHm+LionAWKUwcHov7Qu9XF32J5IKv1x8m0mcnxSMrMLlnc4q4al4bkcJGv/Uiecy3G47w+oIdFI+qQIV9LXAvol9TkcJIv/kiech3G4/y7wXbaV/XC6+9LTDW1dkliYiTKKBF8oj/bgpl5Pxt3FHbk8/6tMRF4SxSqCmgRfKABVtCGfHfrdxe6xY+79uKYm4KZ5HCTgEt4mSLgo8x/Lut/MPnFr7o21rhLCKAAlrEqX7YdpyX5gbT2rs8M/xbUdxd4SwiaRTQIk7y0/YTvPhtMK1qlGemf2tKuDt816OIFAIKaBEnWLrjJC98s4Xm1coys19rShZVOIvI5RTQIrls6Y6TDP1mM02qluHLfq0ppXAWkQwooEVy0Y/bTvBc4GYaVynDf55ug0cxN2eXJCJ5lAJaJJcsCj7GC9+mndb+6uk2lFY4i8g16NyayA0KCIDAQMeWPecZSnitrRSNKc+pda15YO71f/WabQign3vg33PH5WfBweDr6+wqRPIV9aBFblBgYFruXE+M11HCa22lWPQtVPyrNS6pjv1d3M89kAaJDmwgP/D1BT8/Z1chkq+oBy1yE3x9YdWqzF8PXH+E1xZs5846nlkfIawDwHU2ICIFlnrQIjnkq7WHeG3BdjrW89LwnSKSZepBi+SAmWsOMvaHXXRuUJHpvZtTtIjCWUSyRgEtks0+/98Bxv20m66NKjG1V3PN5ywiN0QBLZJNrLV8tCKED3/dS/cmtzL5CV/cXBXOInJjFNAi2cBay7s//0XA/w7wzxZVmPBIU4oonEXkJiigRW5Saqpl1KIdBK4/Qt/bavDmA41wcTHOLktE8jkFtMhNsCaVf323lYXBx3m2Qy1G3FsPYxTOInLzFNAiN8iaFE7V2cLC4DBG3FuP5zrWdnZJIlKAKKBFbkBcYjJh9TaRUDacsT0a0fc2b2eXJCIFjAJaJIvOxifx9KwgEspE4RnSjL63VXV2SSJSACmgRbIg4tx5+s7cwN6wGLz2taBk5K3OLklECijdByLioNCoOB77dC0hp87xed9WCmcRyVHqQYs44K+T0Tw1cwPxiSl8PaAtrbzLO7skESngFNAi1xF0KJL+s4Io7u7KvMG3U6+Sh7NLEpFCQAEtcg3Ld4XxXOBmqpQrzldPt6FquRLOLklECgkFtEgmvtt4lH9/v53GlUvzZb82lC/p7uySRKQQUUCLXMFay6e/H2D80r+4s44nnz7ZkpJF9asiIrlLnzoil0hNtYz7aTcz1hzkwWaVef+xZpouUkScQgEtcsH55BRenreNJVuP43+7N/93f8NMJ70ICIC6vwfwbJlA6JBDBQUHg69vDq1cRPI6BbQIcCYukUFfbWLDoUhe7VafZ+6qec1JLwID4U0CaZAYDPjmTFG+vuDnlzPrFpE8TwEthd6RiDj8Z20gNDKej3o154FmlR1qV7YMFPX1hVWrcrQ+ESmcFNBSqG09eob+/wkiKcUyZ0Bb2vhoABIRyRsU0FJo/borjKHfbMbLoyjf+rehdoVSzi5JRCSdAloKpf/8eYgxS3bSpEoZvniqNV4eRZ1dkojIZRTQUqikpFre+3k3n68+SOcGFZnay5cS7vo1EJG8R59MUmicO5/MsG+3sHz3KZ66rQb/90AjXDO5jUpExNkU0FIohEbFMeA/G9l36hxjezSi723ezi5JROSaFNBS4G06HMkzszdxPjmVWf1ac2cdL2eXJCJyXQpoKdDmbwrl399vp3LZYnw7qLWu1BaRfEMBLQVSaqpl4i97+GTVfm6reQufPNmCsiU0G5WI5B8KaClwYs8n89LcYH7ZFYZf2+qMebARbq6a8EJE8hcFtBQoB8NjeWb2RkJOnePNBxry1O3e1xxTW0Qkr1JAS4Gx4q8wXvw2mCIuhq+ebssddTydXZKIyA1TQEu+l5pqmbYyhEnL99KgUmk+69OSauVLOLssEZGbooCWfC0mIYl/fbeVX3eF8XDzKrzzcBOKu7s6uywRkZumgJZ8K+TUOQbN3sjhiDhGP9AQf33fLCIFiEOXthpjGhtjgowxUcaYicaBT0FjTA9jzAFjTLIxZoMxpvHNlyuSZumOkzw0/Q/OxiXx9YC29Gvno3AWkQLlugFtjCkKLAHWAa2AJoD/ddrUAmYBrwGVgd3AjJsrVQQSk1N564ddDJ6ziVpeJVky9A7+UfMWZ5clIpLtHDnF3Q0oCbxsrT1vjHkVmAZ8eY02DYBXrbXfAhhjpgNrb7ZYKdyOnYnn+cDNbDlyBv/bvfn3ffUpWkTfN4tIweRIQDcD1lhrz194vBVoeK0G1tofrniqIRCS9fLkooAACAx0dhXOE1f2FOG1grHG4nWgBavW3cqqD51XT7MNAfjG/w60d14RIlKgOfIddGng4MUH1loLpBhjyjmyAWNMEWAE8Gkmrw8yxmw0xmw8ffq0I6sslAIDITjY2VXkPksqUdX+4lT9IFwTi1N5xx2UjLzV2WXRz/3CX0t+fs4tREQKLEd60MlAyhXPJQAlgCgH2r9+4b8fZ/SitTYACABo1aqVdWB9hZavL6xa5ewqck9YdAJDA7dw+FAkvdpUZ/QDDSnmlkdOaXcAaA+DBjm5EBEpqBwJ6Eig9hXPeQCJ12tojLkTGA60u+QUuch1/borjFf+u5WEpFQm9WzGw82rOrskEZFc5UhABwF9Lj4wxvgARUkL7kwZY2oA3wFDrbXbb6ZIKTziE1MY99Mu5qw7QqPKpZnyRHNNESkihZIjAf0/oIIxxs9aG0jarVPLrbUpxhhXa+2Vp78xxhQHfgR+AOYbYy5+wsZe+A5b5Cq7T0Tzwjdb2HfqHAPv9OHle+vpKm0RKbSuG9DW2mRjzADgG2PMFMBy4Rs4YIMx5j1r7bwrmnUBGl34GXDJ8z7AoZstWgoWay1f/nGI937+izIl3Jjdvw131vFydlkiIk7l0FCf1trFFwYfaQmss9ZGXHi+ZSbLLwI0rJNc1+mY84z471ZW7TlNp/oVmPBoU24pVdTZZYmIOJ3DY3Fba0+SdtpaJFv8tP0Ery/YTlxiCmN7NKLPP2pouE4RkQs0WYbkujNxifzfop0s3nqcplXL8MFjzahT0cPZZYmI5CkKaMlVv+0O49XvtxMVm8jwe+oyuEMt3FwdmrNFRKRQUUBLrohOSOLtH3bx3cZQ6lfyYFa/1jSqXMbZZYmI5FkKaMlxv+89zWvfb+fE2Xie61iLFzrV0e1TIiLXoYCWHBMZm8hbP+xiwZZj1PIqyfwht9O8ukNDuIuIFHoKaMl21loWBR9n7A+7iElI4oW7a/Nsx9p5ZxxtEZF8QAEt2epoZByvL9zB//aexrdaWcY/0pR6lXSFtohIVimgJVskp6Tyn7WHeX/ZHoyBNx9oSJ/bvHF10X3NIiI3QgEtN23joUjeWLST3Sei6VDPi3EPN6FK2eLOLktEJF9TQMsNCz93nvd+/ov/bgrl1jLF+KR3C7o2rqTRwEREsoECWrIsJdXy9fq009nxSSkM6VCL5zvWpmRRHU4iItlFn6gZCAiAwEBnV3G5ZhsC6Oce+Pc8Yk6yqVRl/s+nMztLVqTd2cOMObic2n9EwnvOrSvXBQeDr6+zqxCRAkwBnYHAwLz3+dvPPZAGicGAr1O2H+pemvHV72KJZwMqnY9h2t7FdI/cU3inLPP1BT8/Z1chIgWYAjoTvr6wapWzq7hEBwDfXC8qJiGJT1bt54s1B3Ex8MKdNXmmfS1KFn0iV+sQESlsFNCSoeSUVL7bGMqHv+4h/FwiDzevwoh761FZV2eLiOQKBbRcxlrLqr2nee+nv9gTFkNr73LMeKo1zaqVdXZpIiKFigJa0m08FMmEpXvYcCiSauWL83HvFnTTbVMiIk6hgBZ2Hj/L+8v2sHLPabw8ivJWj0b0bF0d9yKap1lExFkU0IXYwfBYPvx1L0u2HqdMcTdGdq3PU7fXoIS7DgsREWfTJ3EhdDA8lukrQ1iw5Rjuri4837E2A++qSZnibs4uTURELlBAFyIhp2KYtiKExVuP4+bqQt/bavBsh9p4eRR1dmkiInIFBXQhsPtENNNWhPDTjhMUd3Nl4J01GXBnTQWziEgepoAuwDYdjuLT3/fz664wPIoW4bkOtXn6Dh/Kl3R3dmkiInIdCugCJiXV8uuuk3y++iCbDkdRprgbwzrXod/tPpQpoe+YRUTyCwV0ARGfmMJ/Nx3lizUHORwRR7XyxRnzYCMea1VVV2WLiORD+uTO506eTeDr9YeZs+4wUXFJNKtWlpFd63Nvo0q4umiAERGR/EoBnQ9Za1l3IJLZ6w6xbGcYqdbSuUFFBt1Vk1Y1ymnkLxGRAkABnY+cc3FjwdpDzF53mL1h5yhbwo3+d/jQu211atxS0tnliYhINlJA53HWWnYej2aud2cWeDbk3KKdNKlShgmPNuXBZpUp5ubq7BJFRCQHKKDzqKjYRBYGH+O7jaHsPhGNe4Um3B+xhz6jB+JbraxOY4uIFHAK6DwkJdWyJiSc74KO8uuuMBJTUmlatQxvPdSYB18fRJmU81D9FWeXKSIiuUAB7WQXT2EvCj7Gkq0nOBmdQLkSbjz5jxo81qoqDW4tnbZgynnnFioiIrlKAZ2B50OGUftcMHTIuW0cKFaOxbc0YLFnAw4UL49bagrtzxxkdPhO7o7aT9EVKZc3CA4GX9+cK0hERPIUBXQGvLygVA6sN9S9NEvL12WRZwO2l6qEsZa20UcZeCKIbhF7KZuSkHljX1/w88uBqkREJC9SQGeg/ZbJ2bIeay37Tp1j6Y6TLNt5kp3HowFoWrUMo5pV5v6mlalUpli2bEtERAoWBXQ2S021bA09w9KdJ/llZxgHw2MBaFG9LK/dlzbCl+5ZFhGR61FAZ4MzcYms3hfOyj2n+N/ecMLPnaeIi+G2WrfQ/w4fujSsSIXS6imLiIjjFNA3IDU17crrVXtOsWrvabYciSLVQtkSbtxZx4u763txd72Kmj1KRERumAI6Az0/WwvA3GduA9K+Sz4cEcfaAxH8uT+CtfvDCT+XCKR9n/x8x9q0r1cB32plc2yCiitrEufq+dladp2IpuGtpXPs36Qg/Zs3eXMZANvfvPem13Wt/ZKd28kuGdXrrH/b/HRMqVYFdKbOJ6cwb+NR1h6IYO3+CE6cTbvCuoJHUdrV9qR9XS/uquuFZ6miTq5UREQKIgX0Fc6dTyb46BnOJ6cSfHQb5Uu6c1vNW/hHrVu4reYt1PIqqWE2RUQkxymgr1CqaBHKFHejuJsrn/VtSd0KHrhoXmUREcllCugM+Him3QZVv1JpJ1ciIiKFlYuzCxAREZGrKaBFRETyIAW0iIhIHqSAFhERyYMU0CIiInmQAlpERCQPUkCLiIjkQQ4FtDGmsTEmyBgTZYyZaBwYSutG2oiIiEia6wa0MaYosARYB7QCmgD+2d1GRERE/uZID7obUBJ42Vq7H3gV6J8DbUREROQCRwK6GbDGWnv+wuOtQMMcaCMiIiIXGGvttRcw5gMAa+3wS547DdS11kbdbBtjzCBg0IWHjYEdWX8bhYYnEO7sIvIw7Z9r0/65Nu2fa9P+ubab2T81rLVeVz7pyGQZyUDKFc8lACWADAM6K22stQFAAIAxZqO1tpUDNRVK2j/Xpv1zbdo/16b9c23aP9eWE/vHkVPckcCVye4BJGZzGxEREbnAkYAOAm67+MAY4wMUJS2Es7ONiIiIXOBIQP8PqGCM8bvw+DVgubU2xRjjmtU219lWgAP1FGbaP9em/XNt2j/Xpv1zbdo/15bt++e6F4kBGGMeBL4B4gALdLDW7jLGbALes9bOc7RNdhYvIiJSUDkU0ADGmEpAS2CdtTYip9qIiIhIFsbittaetNb+mJWgdaSNMWapMcb/wv9/boyxl/yEOLotKTwyO050/EhWGWPGG2OWXPJYx5BckzHG/4pj5OLPm9l9/Dh1sgxjTG/g3kueannhcbkLP82dUZezXSOANL55msyOEx0/V8gggHQMXWCMaQo8Cwy75OlCfwxdJ4B0/EAgfx8f5Ui7Y+kosJxsPn6cFtDGmPLAB8CeC4+LAXWA1dbaMxd+YpxVn5Nd9Y+s8c3TZHac6Pi52pUBpGPob8YYF9Iu6pl0YThifQb9LbMAWomOH6y1iZccH2eAfwI7gY1k8/HjzB70B8AC0v6xIe0vDQvsMMbEGWN+NsZUd1p1TnKNDwmNb54ms+NEx88lMgogdAxdajBpAXPIGPPghT9edAxxzQAqh46fy1y4k+k14E1y4PhxSkAbYzoCnYCRlzzdENgF+JE2lncK8HnuV+d0mf0ja3zzNJkdJzp+LpdRAOkYAowxpYAxwAGgBvAS8CfQAh1Dl7kigHT8XK0HcMpau54c+Axy+Cru7HKhh7gNeMla+6MxZhawylo764rlqgOHAE9rbaEZ4MQY0x8YCLxI2sAukwA3LoxRnpUx0QuDzI6Twnr8QHoAHQROAt8DdwGlSRufILWwH0PGmL7Ap0B1a224MaYIsB34yFr78SXLFdpj6CJjzD+BV621bcwNzMtQ0BljlgOzrbX/yeC1mz5+nNGDfgMIstb+eJ3logED3JrzJeUd1toZ1tp/WGvXW2v3kfYd4j2kfcCev2Lxi+ObF2aZHSeF8vi54J+knYrsaK0dTdrxUwJ4Eh1DAFVJ+wwKB7DWJpPWafC5YrnCfAxd9Cww/cL/J6PjJ50xpjJwO2lf1Wbkpo8fZwS0H9DDGHPGGHPmwuOPjTE/GmMeumS51qSd6j2S+yXmKRf/kUPR+OYYY6Zncpz8S8dPuswCyBMdQ5D2u3RlqNQAXtYx9LcMAkhzLFzucWCFtTYarvnZdMPHjyOzWWW3O6/Y7vukXShWAhhvjDkLuAMfAV8VtqsojTHTgV+ttQsvPHXxHzkYeOyS5Qrr+OZbyeA4AdZn9HxhO34uyCyAniOtRwQU6mPoR+AjY8yzwGLSzjj4As+gY+hSlwUQaXMs9Ln4YiE+fi7qBiy95HGGn003dfxYa536A8wi7VJ9Q9qV3WeAw6R991rS2fU5YX8MIu3Ws46k3Wq198I+KgKcAvwuLPc5sMTZ9Tph/2R4nOj4uWwf3QKcJS2MqwIvkHYqsraOofR91A5YS9pQxAeAh3QMXbWPlpF2rdDFx/oM+ntfuAGxQOtLnsv24yfXLxKTa7tw4//7pN2+cJa0i3xGWWtjjcY3FwcZY9qRdhw1I+1isX9ZaxfqGBJHGGPcSAuaDtbaoEue1/GTixTQ+YzR+OZyk3QMyc3Q8ZN7FNAiIiJ5kFPH4hYREZGMKaBFRETyIAW0iIhIHqSAFhERyYMU0CIiInmQAlpERCQP+n/E+vXldhrC6gAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 经验分布\n",
    "\n",
    "import statsmodels.distributions.empirical_distribution as em\n",
    "\n",
    "# 体重数据\n",
    "x = np.array([75.0, 64.0, 47.4, 66.9, 62.2, 62.2, 58.7,\n",
    "                   63.5, 66.6, 64.0, 57.0, 69.0, 56.9, 50.0, 72.0])\n",
    "\n",
    "# 使用statsmodels的经验分布函数ECDF估计总体分布函数\n",
    "ecdf = em.ECDF(x)\n",
    "# 对x进行排序，从小到大，排序使绘制的图像更加美观，不排序会呈现网状图像\n",
    "x.sort()\n",
    "# 生成阶跃函数值\n",
    "F = ecdf(x)\n",
    "\n",
    "# 使用matplotlib的step函数绘制经验分布函数曲线\n",
    "plt.figure(figsize=(8, 6))\n",
    "# x 横坐标 ，F 纵坐标\n",
    "plt.step(x, F, color='b', where='pre', label='经验分布（前点阶跃）')\n",
    "plt.step(x, F, color='r', where='post', label='经验分布（后点阶跃）')\n",
    "# 设置x轴的范围\n",
    "plt.xlim(45, 76)\n",
    "# 设置y轴的范围\n",
    "plt.ylim(0, 1.05)\n",
    "# 绘制垂直线\n",
    "plt.vlines(x, 0, 0.05)\n",
    "\n",
    "# 依据体重数据拟合正态分布曲线：\n",
    "# 1. 从体重数据最大值和最小值，从最大最小值之间每隔0.01生成x轴数据\n",
    "# 2. 根据x数据调用正态分布的累积分布函数生成分布函数值，即y值\n",
    "# 3. 根据生成的x，y值绘制正态分布曲线并和经验分布函数叠加\n",
    "\n",
    "x_min = x.min()\n",
    "x_max = x.max()\n",
    "x_n = np.arange(x_min, x_max, 0.01)\n",
    "\n",
    "# 根据x值获取y轴上的正态分布累积分布函数的值\n",
    "y = st.norm.cdf(x_n, np.mean(x_n), np.std(x_n))\n",
    "plt.plot(x_n, y, label='正态分布')\n",
    "plt.xticks(size=14)\n",
    "plt.yticks(size=14)\n",
    "plt.legend(fontsize=14)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ebe7c2f",
   "metadata": {},
   "source": [
    "#### 1.4.3 QQ图与茎叶图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "57e2526e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# QQ图用来直观判断数据是否服从正态分布\n",
    "# 数据大致分布在直线附近，则认为该数据大致上服从正态分布\n",
    "\n",
    "# 体重数据\n",
    "weights = [75.0, 64.0, 47.4, 66.9, 62.2, 62.2, 58.7,\n",
    "                   63.5, 66.6, 64.0, 57.0, 69.0, 56.9, 50.0, 72.0]\n",
    "\n",
    "# 利用Scipy绘制QQ图\n",
    "st.probplot(weights, plot=plt)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a3235906",
   "metadata": {},
   "outputs": [],
   "source": [
    "# st.probplot??"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d3a21a4a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 手工计算QQ图\n",
    "\n",
    "import scipy.special as ss\n",
    "\n",
    "x = weights\n",
    "n = len(x)\n",
    "phi = np.zeros(n)\n",
    "x = sorted(x)\n",
    "\n",
    "# 计算数据的标准正态分布反函数值，公式见下图：\n",
    "for i in range(1, n+1):\n",
    "    # ndtri函数计算标准正态分布函数的反函数（注意：是分布函数的反函数，不是密度函数的反函数）\n",
    "    phi[i-1] = ss.ndtri((i-0.375)/(n+0.25))\n",
    "\n",
    "# 画出QQ散点图\n",
    "# phi x轴数据，x y轴数据\n",
    "plt.scatter(phi, x)\n",
    "\n",
    "# 绘制QQ图\n",
    "# 使用标准差的无偏估计\n",
    "q_std = st.tstd(x)\n",
    "q_mean = np.mean(x)\n",
    "qx = np.arange(-2, 3)\n",
    "\n",
    "# y = std * x + mu\n",
    "qy = q_std*qx + q_mean\n",
    "plt.xlabel('理论分位数', size=14)\n",
    "plt.ylabel('样本数据', size=14)\n",
    "plt.title('QQ图', size=20)\n",
    "plt.plot(qx, qy, color='r')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4fc70f07",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 | 5\n",
      "4 | 5\n",
      "5 | 0 4 5\n",
      "6 | 1 4 8\n",
      "7 | 2 5 5 8 9\n",
      "8 | 1 3 4 4 4 5 6 6 6 7 9 9 9\n",
      "9 | 0 1 1 2\n",
      "10 | 0\n"
     ]
    }
   ],
   "source": [
    "# 茎叶图（描述性统计分析）\n",
    "\n",
    "# 茎叶图，用于直观展现数据分布的结构；竖线左边是十位数，右边是个位数\n",
    "\n",
    "from itertools import groupby\n",
    "\n",
    "# 某课程的考试分数\n",
    "scores = np.array([25, 45, 50, 54, 55, 61, 64, 68, 72, 75, 75,\n",
    "                  78, 79, 81, 83, 84, 84, 84, 85, 86, 86, 86,\n",
    "                  87, 89, 89, 89, 90, 91, 91, 92, 100])\n",
    "\n",
    "for k, g in groupby(sorted(scores), key=lambda x: x//10):\n",
    "    l = map(str, [int(_) % 10 for _ in list(g)])\n",
    "    print(k, '|', ' '.join(l))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e845b9da",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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